1. Field
The present invention generally relates to techniques for electronic prognostication for computer systems. More specifically, the present invention relates to a method and an apparatus that generates extended data for a pattern-recognition model used in electronic prognostication for a computer system.
2. Related Art
Many computer systems are equipped with a significant number of hardware and software sensors which can be use to monitor performance parameters of the computer system. One use for the monitored performance parameters is electronic prognostication for the computer system using a pattern-recognition model based on nonlinear, nonparametric (NLNP) regression. Typically, the pattern-recognition model is constructed during a training phase in which the correlations among the performance parameters are learned by the model. Then, during operation of the computer system, the pattern-recognition model is used to estimate the value of each performance parameter in the model as a function of the other performance parameters. Significant deviations between the estimates from the model and the monitored performance parameters may indicate a potential incipient degradation mode in the computer system.
One issue that may be encountered when using an NLNP regression pattern-recognition model is that after the training data set is generated during the training phase and used to train the model, there may be configuration changes to the computer system that cause the performance or operational regime of the computer system to shift into a regime outside of that observed during the training phase. However, an NLNP regression pattern-recognition model may not function correctly when operating on input data that falls outside of the training data set.
For example, suppose an NLNP regression pattern-recognition model is trained using a training data set generated from a computer system operating using 2 gigabyte (GB) dual in-line memory modules (DIMMs). If a customer upgrades the computer system by replacing the 2 GB DIMMs with 4 GB DIMMs that draw more power and run hotter, the operating regime of the upgraded computer may cause one or more of the monitored performance parameters, such as temperature, current, or voltage, to go outside the operational regime used during the training phase. This can result in false alarms being generated based on output from the NLNP regression pattern-recognition model, even if the computer system is functioning correctly. Typically, the model would have to be re-trained based on the new configuration. However, the training period can often be as long as 10-14 days for a computer system in the field. Additionally, training may be required each time a customer reconfigures the computer system, extending the length of time the computer system is in a training phase and potentially reducing the amount of time the model can perform electronic prognostication for the computer system.
Hence, what is needed is a method and system that generates extended data for a pattern-recognition model used in electronic prognostication for a computer system without the above-described problems.